Civic Hackathons: New Terrain for Local Government-Citizen Interaction?
Bibliographic record
Abstract
As more and more governments share open data, tech developers respond by creating apps using these data to generate content or provide services that citizens may find useful. More recently, there is an increase in popularity of the civic hackathon. These time-limited events gather tech enthusiasts, government workers and interested citizens, in a collaborative environment to apply government open data in developing software applications that address issues of shared civic importance. Building on the Johnson and Robinson (2014) framework for understanding the civic hackathon phenomenon, Canadian municipal staff with civic hackathon experience were interviewed about their motivations for and benefits derived from participation in these events. Two broad themes emerged from these interviews. First, through the development of prototypical apps using municipal open data and other data sets, civic hackathons help put open data into public use. Second, civic hackathons provide government staff with valuable feedback about municipal open data sets informing and evolving future open data releases. This paper concludes with reflections for urban planners about how civic hackathons might be used in their practice and with recommendations for municipal staff considering using civic hackathons to add value to municipal open data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.031 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".